Evaluating the Effect of Linguistic Relatedness on Cross-Lingual Transfer in Large Multilingual Automatic Speech Recognition
arXiv:2607.04814v1 Announce Type: cross Abstract: Extending automatic speech recognition (ASR) to low-resource African languages is constrained by the prohibitive demands of data collection at scale. A promising direction is to leverage linguistic relatedness to enhance cross-lingual transfer from a related auxiliary language to the low-resource target by sequentially adapting on both. Although this strategy has shown meaningful improvements in small ASR models, its effectiveness in large ASR remains unclear. We extend this framework to large multilingual ASR through a systematic controlled ex
The proliferation of large language models and foundation models is driving research into optimizing their application for diverse linguistic contexts, particularly in underserved regions. This research aims to address current limitations in ASR for low-resource languages.
Improving ASR for low-resource African languages can unlock significant economic and social potential, fostering digital inclusion and enabling new applications in healthcare, education, and commerce. It also addresses a critical gap in global AI accessibility.
The effectiveness of cross-lingual transfer in large multilingual ASRs, particularly when leveraging linguistic relatedness, is being systematically validated. This could provide a more efficient pathway to develop robust AI tools for a wider array of languages.
- · African language communities
- · AI developers focused on emerging markets
- · Multilingual ASR technology providers
- · Monolingual AI solutions with limited scalability
- · Current expensive data collection methods for ASR
Enhanced accessibility and utility of voice-activated technologies and AI services in numerous African languages.
Accelerated digital transformation and economic growth in regions previously underserved by advanced AI capabilities.
Reduced digital divide, fostering greater participation in the global digital economy and potentially supporting unique cultural expressions through technology.
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Read at arXiv cs.AI